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Supplementary materials for "ProppLearner: Deeply Annotating a Corpus of Russian Folktales to Enable the Machine Learning of a Russian Formalist Theory"
(2015-12-02)
This archive contains the supplementary material for the journal article "ProppLearner: Deeply Annotating a Corpus of Russian Folktales to Enable the Machine Learning of a Russian Formalist Theory", published in the Journal ...
Big Data Privacy Scenarios
(2015-10-01)
This paper is the first in a series on privacy in Big Data. As an outgrowth of a series of workshops on the topic, the Big Data Privacy Working Group undertook a study of a series of use scenarios to highlight the challenges ...
Keys Under Doormats: Mandating insecurity by requiring government access to all data and communications
(2015-07-06)
Twenty years ago, law enforcement organizations lobbied to require data and communication services to engineer their products to guarantee law enforcement access to all data. After lengthy debate and vigorous predictions ...
Dynamic Prefetching of Data Tiles for Interactive Visualization
(2015-10-19)
In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client-server architecture, where the user interacts with a lightweight client-side interface to ...
Network Maximal Correlation
(2015-09-21)
Identifying nonlinear relationships in large datasets is a daunting task particularly when the form of the nonlinearity is unknown. Here, we introduce Network Maximal Correlation (NMC) as a fundamental measure to capture ...
Designing a Context-Sensitive Context Detection Service for Mobile Devices
(2015-09-24)
This paper describes the design, implementation, and evaluation of Amoeba, a context-sensitive context detection service for mobile devices. Amoeba exports an API that allows a client to express interest in one or more ...
PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling
(2015-07-02)
Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource ...
Representation Discovery for Kernel-Based Reinforcement Learning
(2015-11-24)
Recent years have seen increased interest in non-parametric reinforcement learning. There are now practical kernel-based algorithms for approximating value functions; however, kernel regression requires that the underlying ...
Prophet: Automatic Patch Generation via Learning from Successful Patches
(2015-07-13)
We present Prophet, a novel patch generation system that learns a probabilistic model over candidate patches from a database of past successful patches. Prophet defines the probabilistic model as the combination of a ...
Deep Learning without Poor Local Minima
(2016-05-23)
In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, ...